Infra AI Manager-Manager

Designation-Infra AI Manager

Job Description

• Architect and lead the development of AI-integrated infrastructure platforms, supporting both classical ML and generative AI workloads across Azure, AWS, or GCP.
• Drive end-to-end lifecycle management of AI/ML models, including deployment, monitoring, retraining, and scaling using tools like Kubeflow, MLflow, SageMaker, Azure ML, and TensorFlow Serving.
• Build and optimize data lakehouse architectures, pipelines, and distributed systems for high-throughput AI workloads.
• Lead the implementation of Infrastructure as Code (IaC) using Terraform, Pulumi, and Ansible, ensuring modularity, reusability, and compliance.
• Develop and integrate AI-enhanced observability frameworks using Prometheus, Grafana, ELK Stack, and OpenTelemetry for predictive alerting and anomaly detection.
• Oversee cloud migration and modernization initiatives, applying AI-driven insights for workload placement, performance tuning, and cost optimization.
• Establish and enforce DevSecOps practices, embedding security, governance, and compliance into infrastructure automation workflows.
• Collaborate with Data Science, DevOps, and Product teams to align infrastructure capabilities with AI model requirements and business objectives.
• Lead the adoption of MLOps best practices, including CI/CD for model deployment, versioning, and rollback strategies.
• Manage and mentor a team of engineers, fostering a culture of innovation, technical excellence, and continuous improvement.
• Balance short-term delivery goals with long-term infrastructure strategy, ensuring scalability, reliability, and performance across AI systems.

Desired Profile

• 10+ years of experience in infrastructure engineering, cloud automation, or AI platform development.
• Proven leadership in designing and scaling AI/ML infrastructure across cloud-native environments.
• Deep hands-on expertise in Python, Terraform, Kubernetes, and containerized AI workloads.
• Strong experience with AI/ML platforms: Kubeflow, MLflow, Azure ML, SageMaker, TensorFlow, PyTorch.
• Expertise in cloud platforms (Azure, AWS, GCP) and hybrid cloud architecture.
• Familiarity with data lakehouse technologies, distributed data processing, and scalable storage systems.
• Experience with CI/CD pipelines, GitOps, and automated model deployment workflows.
• Strong understanding of security frameworks, compliance standards, and infrastructure governance.
• Excellent communication and stakeholder management skills, with the ability to align technical strategy with business outcomes..

Experience

• 10 years and above

Education

• B.Tech. / BS in Computer Science

Technical Skills & Certifications

• Azure Solutions Architect Expert, AWS Certified Machine Learning – Specialty, or Google Cloud ML Engineer
• HashiCorp Certified Terraform Associate
• Certified Kubernetes Administrator (CKA)
• Certified MLOps Professional (e.g., MLflow, TFX)